BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs
with Multi-turn Health Conversations Polished by ChatGPT
- URL: http://arxiv.org/abs/2310.15896v2
- Date: Mon, 4 Dec 2023 09:26:22 GMT
- Title: BianQue: Balancing the Questioning and Suggestion Ability of Health LLMs
with Multi-turn Health Conversations Polished by ChatGPT
- Authors: Yirong Chen, Zhenyu Wang, Xiaofen Xing, huimin zheng, Zhipei Xu, Kai
Fang, Junhong Wang, Sihang Li, Jieling Wu, Qi Liu, Xiangmin Xu
- Abstract summary: Large language models (LLMs) have performed well in providing general and extensive health suggestions in single-turn conversations.
We propose BianQue, a ChatGLM-based LLM finetuned with the self-constructed health conversation dataset BianQueCorpus.
- Score: 19.502907861059604
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large language models (LLMs) have performed well in providing general and
extensive health suggestions in single-turn conversations, exemplified by
systems such as ChatGPT, ChatGLM, ChatDoctor, DoctorGLM, and etc. However, the
limited information provided by users during single turn results in inadequate
personalization and targeting of the generated suggestions, which requires
users to independently select the useful part. It is mainly caused by the
missing ability to engage in multi-turn questioning. In real-world medical
consultations, doctors usually employ a series of iterative inquiries to
comprehend the patient's condition thoroughly, enabling them to provide
effective and personalized suggestions subsequently, which can be defined as
chain of questioning (CoQ) for LLMs. To improve the CoQ of LLMs, we propose
BianQue, a ChatGLM-based LLM finetuned with the self-constructed health
conversation dataset BianQueCorpus that is consist of multiple turns of
questioning and health suggestions polished by ChatGPT. Experimental results
demonstrate that the proposed BianQue can simultaneously balance the
capabilities of both questioning and health suggestions, which will help
promote the research and application of LLMs in the field of proactive health.
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